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2007国际机械工程与力学会议

作者： Zhaohui REN Naihui SONG Xiaopeng LI Bangchun WEN School mechanical Engineering and Automation Northeastern University

There are a lot of kinds of mechanical equipment to a process of the same material. It is very important to choose a suitable mechanical product in order to reach product quality requirement. Some conventional methodological problems of mechanical product selection are discussed. Fuzzy set theory combined with case based reasoning (FCBR) is applied on the process of mechanical product selection. In the paper, system structure for mechanical product selection and corresponding algorithm based on FCBR are developed. The simulation result of illustrated example shows that the presented algorithm can help customers and developers of mechanical products to select a suitable mechanical product taking account of the effects of various factors, which can avoid unexpected loss because of selection of a wrong mechanical product, and proved to be more reasonable than conventional selection method.

关键词： Mechanical product selection algorithm Fuzzy Set Case Based Reasoning (CBR)

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selection and replacement algorithms for memory performance improvement in Spark

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第8期28卷 2473-2486页

作者： Duan, Mingxing Li, Kenli Tang, Zhuo Xiao, Guoqing Li, Keqin Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

As a parallel computation framework, Spark can cache repeatedly resilient distribution datasets (RDDs) partitions in different nodes to speed up the process of computation. However, Spark does not have a good mechanism to select reasonable RDDs to cache their partitions in limited memory. In this paper, we propose a novel selection algorithm, by which Spark can automatically select the RDDs to cache their partitions in memory according to the number of use for RDDs. Our selection algorithm speeds up iterative computations. Nevertheless, when many new RDDs are chosen to cache their partitions in memory while limited memory has been full of them, the system will adopt the least recently used (LRU) replacement algorithm. However, the LRU algorithm only considers whether the RDDs partitions are recently used while ignoring other factors such as the computation cost and so on. We also put forward a novel replacement algorithm called weight replacement (WR) algorithm, which takes comprehensive consideration of the partitions computation cost, the number of use for partitions, and the sizes of the partitions. Experiment results show that with our selection algorithm, Spark calculates faster than without the algorithm, and we find that Spark with WR algorithm shows better performance. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： iterative computation memory resilient distribution datasets selection algorithm spark weight replacement algorithm

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Combined Antenna selection and Beamforming in Cross-Layer Design for Cognitive Networks

Combined Antenna Selection and Beamforming in Cross-Layer De...

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IEEE International Conference on Communications

作者： Amiotosh Ghosh Walaa Hamouda Department of Electrical and Computer Engineering Concordia University

ISBN: (纸本)9781457720529

We investigate the performance of an antenna selection algorithm applied with precoding to improve the throughput performance of cognitive multiple-input multiple-output (MIMO) radios. We assume cognitive MIMO radios coexist in the same frequency band with the primary users. In such event, for concurrent spectrum access, cognitive nodes use beamforming techniques to cancel the mutual information between cognitive and primary users. In that, cognitive nodes exploit the degrees of freedom offered by MIMO systems to beamform the transmitted signal in an attempt to cancel interference at primary users. In the cross-layer design, cognitive nodes maximize an objective function for the link layer throughput where precoding is applied at the physical layer on the transmitted spatial multiplexed signals. We present a closed form solution for the overall throughput in-terms of the physical layer parameters. Numerical results are presented to show the efficacy of the proposed scheme for different network settings.

关键词： Cross layer design Beamforming multiple input multiple output systems prediction coding selection algorithm combined antenna Physical layer Throughput Frequency bands Radio layer design Closed-form solutions Cancel Ganglia Consumers

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Robust Sparse Subspace Learning for Unsupervised Feature selection

Robust Sparse Subspace Learning for Unsupervised Feature Sel...

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International Joint Conference on Neural Networks

作者： Feng Wang Qi Rao Yongquan Zhang Xu Chen State Key Laboratory of Software Engineering Computer School Wuhan University Institute of Computational Linguistics Peking University

ISBN: (纸本)9781509006212

Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural learning, but also try to avoid these noises induced during the feature selection process. We utilize the feature structural learning to select the discriminant features and use the robust methods to make selected features more reliable. Furthermore, we propose a new unsupervised feature selection algorithm, namely Robust Sparse Subspace Learning Feature selection (RSS). And we employ a coordinate descendent algorithm to solve the RSS formulation. Experiments are conducted on several popular datasets to validate the effectiveness of our proposed algorithm and results show that this RSS algorithm achieves better results than traditional feature selection algorithms in most cases.

关键词： Feature extraction Process selection dimensionality reduction Noise selection algorithm RSS gene Really Simple Syndication REACTOR SAFETY STUDY Subspace

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Bandwidth-satisfied relay-selection scheme for wireless multicast networks with cooperative communications

Bandwidth-satisfied relay-selection scheme for wireless mult...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Yi Chen Ge Gao Shengbin Liao Huaxiong Yao School of Computer Science Huazhong Normal University Wuhan China School of Computer Science Wuhan University China National Engineering Research Center Huazhong Normal University Wuhan China

ISBN: (纸本)9781510821279

Cooperative communication is an efficient technology to achieve high bandwidth demands and wide error-prone client coverage. However, the previous cooperative multicast protocols suffer from bandwidth-violation problem while selecting relay nodes, namely, the cooperative hidden route problem (CHRP) by distributed protocols in wireless multicast networks. In this paper, a novel relay selection algorithm that can avoid the CHRP is proposed, and a bandwidth-satisfied multicast cooperative relay selection scheme is constructed. Moreover, to optimize the cooperative performance, we provide theory analysis for selecting a relay. Simulations results show that the proposed algorithm can efficiently mitigate multicast transmission errors and improve network throughput in a distributed wireless environment by more than 10%.

关键词： Cooperative Communications Multicast Relay-selection Wireless Networks Multicast cooperative communication Wireless networks CYHR1 gene distributed protocol relay Multicast protocols selection algorithm wireless Network throughput

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On the self representation based on the adaptive self radius

On the self representation based on the adaptive self radius

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4th International Workshop on Advanced Computational Intelligence, IWACI 2011

作者： Zhang, Jie Luo, Wenjian School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 Anhui China

ISBN: (纸本)9781612843735

Self representation problem is very important for the negative selection algorithms. In this paper, two methods, named as K-NN-ASR and B-NN-ASR, are proposed to calculate the self radius of a self sample in the continuous space. As for the K-NN-ASR, the self radius of a self sample is calculated according to the distances from itself to the surrounding self samples. In the B-NN-ASR, both the position of the self sample and the distances to the surrounding self samples are considered. Experiments are done to test the performance of the proposed methods. Experimental results demonstrate that the proposed methods are competitive, and the B-NN-ASR is better. © 2011 IEEE.

关键词： selection algorithm Self tuning CONTINUOUS SPACE

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Insense: Incoherent Sensor selection for Sparse Signals

Insense: Incoherent Sensor Selection for Sparse Signals

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Amirali Aghazadeh Mohammad Golbabaee Andrew Lan Richard Baraniuk Stanford University University of Edinburgh Princeton University Rice University

ISBN: (纸本)9781538646595

Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using three datasets, including a real-world dataset on microbial diagnostics, we demonstrate the superior performance of Insense for sparse-signal sensor selection.

关键词： Sensor selection coherence optimization compressive sensing Sensors Compressed sensing signals coherence Subset dimensions selection algorithm Linear measurement

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Backhaul-Aware Optimal Access selection for Traffic Offloading in Small-Cell Networks

Backhaul-Aware Optimal Access Selection for Traffic Offloadi...

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IEEE International Conference on Communications Workshops

作者： Haohan Chai Liping Qian Yuan Wu Haibo Zhou Weidang Lu Jianchao Zheng Changsheng Yu College of Information Engineering Zhejiang University of Technology Department of Electrical and Computer Engineering University of Waterloo Institute of Communications Engineering PLA University of Science and Technology Nokia Solutions and Networks System Technology Co. Ltd

ISBN: (纸本)9781509015269

The limited backhaul capacity has been considered as an important issue that influences the performance of traffic offloading in small-cell networks. We thus investigate how mobile users (MUs) efficiently offload traffic to different small cells with the new paradigm of dual-connectivity, subject to different backhaul capacities of small cells. Specifically, we formulate an optimization problem that jointly optimizes the MUs' access-selections, traffic scheduling, and transmit-power allocations, with the objective of minimizing all MUs' mobile data cost. To tackle with the difficulty due to the mixed binary nonconvex optimization problem, we decompose the joint optimization problem into a subproblem that optimizes the MUs' traffic scheduling and power allocations and a top problem that optimizes the MUs' access-selections. Based on our previous study [1], we can efficiently solve the subproblem by exploiting its hidden monotonicity. With the solution of the subproblem, we further propose two effective algorithms (namely, a simulated annealing based algorithm and a fast access-selection algorithm) to solve the top problem. The key idea of the fast access-selection algorithm is to exploit the information of limited backhaul capacities and properly remove the infeasible selection to reduce the search space. Numerical results validate the effectiveness of our proposed algorithms and the performance advantage of the proposed joint resource allocations for traffic offloading.

关键词： UNLOADING Muscae Mus Traffic return stroke power splitting optimal access Resource utilization selection algorithm algorithms

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Efficient Antenna selection and User Scheduling in 5G Massive MIMO-NOMA System

Efficient Antenna Selection and User Scheduling in 5G Massiv...

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IEEE Vehicular Technology Conference

作者： Xin Liu Xianbin Wang Department of Electrical and Computer Engineering The University of Western Ontario

ISBN: (纸本)9781509016990

To achieve extremely high spectral efficiency in the 5-th generation (5G) communication network, the combination of massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technologies becomes a promising solution. However, due to limited radio frequency (RF) chains and channel condition variation, it is important to develop efficient antenna selection and user scheduling algorithms in complex MIMO-NOMA system. In this paper, efficient antenna selection and user scheduling algorithms are investigated to maximize the sum rate in two MIMO-NOMA scenarios. In the first simple single-band two-user scenario, the proposed antenna selection algorithm achieves higher search efficiency by limiting the candidate antennas to those are beneficial to the relevant users. In the multi-band multi-user scenario, the proposed joint antenna and user (AU) contribution algorithm considers the contribution of each antenna's and user's channel gain to total channel gain jointly. Numerical results show that proposed antenna selection algorithm achieves near-optimal performance, and joint AU contribution algorithm achieves similar performance to existing methods with reduced complexity.

关键词： ANTENNA Scheduling algorithms selection algorithm antennae multiple input multiple output systems Consumers Gold African Union ASTRONOMICAL UNIT Gain radiofrequency

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ADAPTIVE LINK selection STRATEGIES FOR DISTRIBUTED ESTIMATION IN DIFFUSION WIRELESS NETWORKS

ADAPTIVE LINK SELECTION STRATEGIES FOR DISTRIBUTED ESTIMATIO...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Songcen Xu Rodrigo C. de Lamare H. Vincent Poor Communications Research Group Department of Electronics University of York U.K. Dept. of Electrical Engineering Princeton University Princeton NJ 08544 USA

ISBN: (纸本)9781479903573

In this work, we propose adaptive link selection strategies for distributed estimation in diffusion-type wireless networks. We develop an exhaustive search-based link selection algorithm and a sparsity-inspired link selection algorithm that can exploit the topology of networks with poor-quality links. In the exhaustive search-based algorithm, we choose the set of neighbors that results in the smallest mean square error (MSE) for a specific node. In the sparsity-inspired link selection algorithm, a convex regularization is introduced to devise a sparsity-inspired link selection algorithm. The proposed algorithms have the ability to equip diffusion-type wireless networks and to significantly improve their performance. Simulation results illustrate that the proposed algorithms have lower MSE values, a better convergence rate and significantly improve the network performance when compared with existing methods.

关键词： Adaptive link selection diffusion networks wireless sensor networks distributed processing Distributed processing Mean square error selection algorithm Wireless sensor networks Network Signaling Link selection Radio Wireless networks diffusion

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